by Usama Fayyad and Hamit Hamutcu

Published by https://hdsr.mitpress.mit.edu/ on July 28, 2022

Abstract

The lack of an agreed-upon classification of job roles related to data science is causing much confusion that is challenging to the industry, educational sector, and practitioners. Prior work in this area has considered different aspects from different fields or points of view and has shown that more detail is needed in subcategorizing data science professionals. However, other prior work has also shown that avoiding the detailed subcategorization leads to challenging problems, for example, the pursuit of the elusive ‘data science unicorns.’ In this article, we target a simplification of prior work and an anchor categorization of job roles with clear definitions and expectations from each. We achieve this through analysis of survey results, LinkedIn profiles and job descriptions, and in-depth interviews with managing and hiring executives in data science. We also use our judgment as long-term practitioners and employers of data scientists to provide a practice-guided view of the problem.

Our analysis has led to a simplification into three key role families with complementary skills: data analyst, data scientist, and data engineer. We believe this anchor categorization helps resolve several problems, including recruiting, forming, training, managing, and retaining effective data science teams. Although we realize there are and will continue to be many variations of these proposed anchor roles, this simplification is an effective tool to bypass the data science unicorn issue, and it can be used as a basis to establish more specialized or domain-specific roles. The combined skills in these role categories converge on the body of knowledge specification from the Initiative for Analytics and Data Science Standards (IADSS) data science knowledge framework (Fayyad & Hamutcu, 2020). The concise and familiar role categories simplify the problem and decompose it into more solvable subchallenges. We describe the essential knowledge required for each role and how, when, and in what ways it can be varied and extended. This description helps align expectations and serves as a step to tackle the pressing issue of training, evaluating, and building effective data science teams.

Keywords: data science, job roles, skill requirements in data science, categorization of roles, data science unicorns

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